Weka

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Weka

  1. 1. WEKAWEKA A . Antony Alex MCA Dr G R D College of Science – CBE Tamil Nadu - India
  2. 2. Waikato Environment forWaikato Environment for Knowledge AnalysisKnowledge Analysis A collection of open source ML algorithms ◦ pre-processing ◦ classifiers ◦ clustering ◦ association rule It’s a data mining/machine learning tool developed byIt’s a data mining/machine learning tool developed by Department of Computer Science, University of Waikato, New Zealand. Weka is also a bird found only on the islands of New Zealand. Java based Routines are implemented as classes and logically arranged in packages Comes with an extensive GUI interface
  3. 3. Download and Install WEKADownload and Install WEKA Website: http://www.cs.waikato.ac.nz/~ml/weka/index. html Support multiple platforms (written in java):Support multiple platforms (written in java): ◦ Windows, Mac OS X and Linux 39/8/2012
  4. 4. Main FeaturesMain Features 49 data preprocessing tools 76 classification/regression algorithms 8 clustering algorithms 3 algorithms for finding association rules 15 attribute/subset evaluators + 10 search15 attribute/subset evaluators + 10 search algorithms for feature selection 49/8/2012
  5. 5. • Dataset • Classifier • Weka.filters • Weka.classifiers java weka.core.converters.CSVLoader data.csv > data.arff Command line interface java weka.core.converters.CSVLoader data.csv > data.arff java weka.core.converters.C45Loader c45_filestem > data.arff java weka.classifiers.rules.ZeroR -t weather.arff java weka.classifiers.trees.J48 -t weather.arff java weka.filters.supervised.attribute.Discretize -i data/iris.arff -o iris-nom.arff -c last java weka.filters.supervised.attribute.Discretize -i data/cpu.arff -o cpu-classvendor-nom.arff -c first
  6. 6. Main GUIMain GUI Three graphical user interfaces ◦ “The Explorer” (exploratory data analysis) ◦ “The Experimenter” (experimental environment) ◦ “The KnowledgeFlow” (new process◦ “The KnowledgeFlow” (new process model inspired interface) 69/8/2012
  7. 7. Explorer: preExplorer: pre--processing the dataprocessing the data Data can be imported from a file in various formats:ARFF, CSV, C4.5, binary Data can also be read from a URL or from an SQL database (using JDBC) Pre-processing tools inWEKA are called 9/8/2012 7 Pre-processing tools inWEKA are called “filters” WEKA contains filters for: ◦ Discretization, normalization, resampling, attribute selection, transforming and combining attributes, …
  8. 8. DatabaseUtils.props.hsql - HSQLDB DatabaseUtils.props.msaccess - MS Access jdbcDriver jdbcURL ACCESSING DATABASEACCESSING DATABASE DatabaseUtils.props.mssqlserver - MS SQL Server DatabaseUtils.props.mysql - MySQL DatabaseUtils.props.odbc - ODBC access via ODBC/JDBC bridge, DatabaseUtils.props.oracle - Oracle 10g DatabaseUtils.props.postgresql - PostgreSQL 7.4 DatabaseUtils.props.sqlite3 - sqlite 3.x
  9. 9. @relation heart-disease-simplified @attribute age numeric @attribute sex { female, male} @attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina} @attribute cholesterol numeric @attribute exercise_induced_angina { no, yes} WEKA “flat” filesWEKA “flat” files 9/8/2012 9 @attribute exercise_induced_angina { no, yes} @attribute class { present, not_present} @data 63,male,typ_angina,233,no,not_present 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_present ...
  10. 10. 9/8/2012 University of Waikato 10
  11. 11. 9/8/2012 University of Waikato 11
  12. 12. 9/8/2012 University of Waikato 12
  13. 13. 9/8/2012 University of Waikato 13
  14. 14. WEKA:: Explorer: building “classifiers”WEKA:: Explorer: building “classifiers” Classifiers in WEKA are models for predicting nominal or numeric quantities Implemented learning schemes include: ◦ Decision trees and lists, instance-based classifiers, support vector machines, multi-layer perceptrons, logistic regression, Bayes’ nets, … support vector machines, multi-layer perceptrons, logistic regression, Bayes’ nets, … “Meta”-classifiers include: ◦ Bagging, boosting, stacking, error-correcting output codes, locally weighted learning, …
  15. 15. Explorer: clustering dataExplorer: clustering data WEKA contains “clusterers” for finding groups of similar instances in a dataset Implemented schemes are: ◦ k-Means, EM, Cobweb, X-means, FarthestFirst 9/8/2012 16 ◦ k-Means, EM, Cobweb, X-means, FarthestFirst Clusters can be visualized and compared to “true” clusters
  16. 16. Explorer: finding associationsExplorer: finding associations WEKA contains an implementation of the Apriori algorithm for learning association rules ◦ Works only with discrete data Can identify statistical dependencies between 9/8/2012 17 Can identify statistical dependencies between groups of attributes: ◦ milk, butter ⇒ bread, eggs (with confidence 0.9 and support 2000) Apriori can compute all rules that have a given minimum support and exceed a given confidence
  17. 17. Explorer: attribute selectionExplorer: attribute selection Panel that can be used to investigate which (subsets of) attributes are the most predictive ones Attribute selection methods contain two parts: 9/8/2012 18 ◦ A search method: best-first, forward selection, random, exhaustive, genetic algorithm, ranking ◦ An evaluation method: correlation-based, wrapper, information gain, chi-squared, … Very flexible:WEKA allows (almost) arbitrary combinations of these two
  18. 18. Explorer: data visualizationExplorer: data visualization Visualization very useful in practice: e.g. helps to determine difficulty of the learning problem WEKA can visualize single attributes (1-d) and pairs of attributes (2-d) ◦ To do: rotating 3-d visualizations (Xgobi-style) 9/8/2012 19 ◦ To do: rotating 3-d visualizations (Xgobi-style) Color-coded class values “Jitter” option to deal with nominal attributes (and to detect “hidden” data points) “Zoom-in” function
  19. 19. Thank UThank U

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